On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification. - Jakob Gawlikowski; Michael Schmitt 003; Anna M. Kruspe; Xiaoxiang Zhu 001 (IGARSS, 2020)

Towards Out-of-Distribution Detection for Remote Sensing. - Jakob Gawlikowski; Sudipan Saha; Anna M. Kruspe; Xiao Xiang Zhu 001 (IGARSS, 2021)

Out-of-distribution detection in satellite image classification. - Jakob Gawlikowski; Sudipan Saha; Anna M. Kruspe; Xiao Xiang Zhu 001 (CoRR, 2021)

Leveraging Evidential Deep Learning Uncertainties with Graph-based Clustering to Detect Anomalies. - Sandeep Kumar Singh 002; Jaya Shradha Fowdur; Jakob Gawlikowski; Daniel Medina (CoRR, 2021)

Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification. - Jakob Gawlikowski; Sudipan Saha; Julia Niebling; Xiao Xiang Zhu 001 (IGARSS, 2022)

Estimating Uncertainty of Deep Learning Multi-Label Classifications Using Laplace Approximation. - Ferdinand Rewicki; Jakob Gawlikowski (IGARSS, 2022)

Compact Feature Representation for Unsupervised Ood Detection. - Sudipan Saha; Jakob Gawlikowski; Jay Nandy; Xiao Xiang Zhu 001 (IGARSS, 2022)

The impact of averaging logits over probabilities on ensembles of neural networks. - Cedrique Rovile Njieutcheu Tassi; Jakob Gawlikowski; Auliya Unnisa Fitri; Rudolph Triebel (AISafety@IJCAI, 2022)

Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks. - Frank Nussbaum; Jakob Gawlikowski; Julia Niebling (NeurIPS, 2022)

Explaining the Effects of Clouds on Remote Sensing Scene Classification. - Jakob Gawlikowski; Patrick Ebel 002; Michael Schmitt 003; Xiao Xiang Zhu 001 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

An Advanced Dirichlet Prior Network for Out-of-Distribution Detection in Remote Sensing. - Jakob Gawlikowski; Sudipan Saha; Anna M. Kruspe; Xiao Xiang Zhu 001 (IEEE Trans. Geosci. Remote. Sens., 2022)

Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Maritime Trajectories. - Sandeep Kumar Singh 002; Jaya Shradha Fowdur; Jakob Gawlikowski; Daniel Medina (IEEE Trans. Intell. Transp. Syst., 2022)

The Unreasonable Effectiveness of Deep Evidential Regression. - Nis Meinert; Jakob Gawlikowski; Alexander Lavin (AAAI, 2023)

A survey of uncertainty in deep neural networks. - Jakob Gawlikowski; Cedrique Rovile Njieutcheu Tassi; Mohsin Ali; Jongseok Lee; Matthias Humt; Jianxiang Feng; Anna M. Kruspe; Rudolph Triebel; Peter Jung 001; Ribana Roscher; Muhammad Shahzad 002; Wen Yang 001; et al. (Artif. Intell. Rev., 2023)

Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification. - Jakob Gawlikowski; Sudipan Saha; Julia Niebling; Xiao Xiang Zhu 001 (EURASIP J. Adv. Signal Process., 2023)

Efficient Data Source Relevance Quantification for Multi-Source Neural Networks. - Jakob Gawlikowski; Nina Maria Gottschling (BMVC, 2024)

Exploiting Text-Image Latent Spaces for the Description of Visual Concepts. - Laines Schmalwasser; Jakob Gawlikowski; Joachim Denzler; Julia Niebling (ICPR (33), 2024)

Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-Regenerative Life Support System Telemetry. - Ferdinand Rewicki; Jakob Gawlikowski; Julia Niebling; Joachim Denzler (ECML/PKDD (9), 2024)

Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning. - Nils Lehmann; Jakob Gawlikowski; Adam J. Stewart; Vytautas Jancauskas; Stefan Depeweg; Eric T. Nalisnick; Nina Maria Gottschling (CoRR, 2024)